OpenRaiser/Intern-Atlas huggingface dataset:
Induction Models on \mathbb{N}
Venue: arXiv cs.LO 2020
Year: 2020

Mathematical induction is a fundamental tool in computer science and mathematics. Henkin initiated the study of formalization of mathematical induction restricted to the setting when the base case B is set to singleton set containing 0 and a unary generating function S. The usage of mathematical induction often involves wider set of base cases and k-ary generating functions with different structural restrictions. While subsequent studies have shown several Induction Models to be equivalent, there does not exist precise logical characterization of reduction and equivalence among different Induction Models. In this paper, we generalize the definition of Induction Model and demonstrate existence and construction of S for given B and vice versa. We then provide a formal characterization of the reduction among different Induction Models that can allow proofs in one Induction Models to be expressed as proofs in another Induction Models. The notion of reduction allows us to capture equivalence among Induction Models.
Mastering Rate based Curriculum Learning
Venue: arXiv cs.LG 2020
Year: 2020

Recent automatic curriculum learning algorithms, and in particular Teacher-Student algorithms, rely on the notion of learning progress, making the assumption that the good next tasks are the ones on which the learner is making the fastest progress or digress. In this work, we first propose a simpler and improved version of these algorithms. We then argue that the notion of learning progress itself has several shortcomings that lead to a low sample efficiency for the learner. We finally propose a new algorithm, based on the notion of mastering rate, that significantly outperforms learning progress-based algorithms.
Feedback Attention for Cell Image Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we address cell image segmentation task by Feedback Attention mechanism like feedback processing. Unlike conventional neural network models of feedforward processing, we focused on the feedback processing in human brain and assumed that the network learns like a human by connecting feature maps from deep layers to shallow layers. We propose some Feedback Attentions which imitate human brain and feeds back the feature maps of output layer to close layer to the input. U-Net with Feedback Attention showed better result than the conventional methods using only feedforward processing.
An Evaluation Framework of End-to-End 5G Millimeter Wave Communication for Connected Vehicle Applications
Venue: arXiv cs.NI 2020
Year: 2020

The internet-of-things (IoT) environment connects different intelligent devices together and enables seamless data communication between the connected devices. Connected vehicles (CVs) are one of the primary example of the IoT, and the efficient, reliable, and safe operation of CVs demands a reliable wireless communication system, which can ensure high throughput and low communication latency. The 5G millimeter wave (5G mmWave) wireless communication network offers such benefits, which can be the enabler of CV applications, especially for dense urban areas with high number of CVs. In this study, we present a simulation-based evaluation framework of end-to-end 5G mmWave communication for CV applications. In addition, we compare the 5G mmWave with the Dedicated Short Range Communication (DSRC) technology for a CV application. The simulation framework is developed using two simulators, a network simulator and a traffic simulator. In order to develop the framework in this study, we have used Network Simulator 3 (ns-3) and SUMO, an open-source microscopic roadway traffic simulator. We have used end-to-end latency, packet loss and throughput as the performance evaluation metrics. We have found that for dense urban areas, 5G mmWave can achieve higher throughput, lower latency and lower data loss compared to DSRC. 5G mmWave can support CV applications with high throughput requirement on the downlink data flow. Through further investigation, we have found that the performance of 5G mmWave is significantly impacted by the penetration level of CVs, maximum CV speed, and CV application requirements.
New Techniques for Proving Fine-Grained Average-Case Hardness
Venue: arXiv cs.CC 2020
Year: 2020

The recent emergence of fine-grained cryptography strongly motivates developing an average-case analogue of Fine-Grained Complexity (FGC). This paper defines new versions of OV, $k$SUM and zero-$k$-clique that are both worst-case and average-case fine-grained hard assuming the core hypotheses of FGC. We then use these as a basis for fine-grained hardness and average-case hardness of other problems. The new problems represent their inputs in a certain ``factored'' form. We call them ``factored''-OV, ``factored''-zero-$k$-clique and ``factored''-$3$SUM. We show that factored-$k$-OV and factored $k$SUM are equivalent and are complete for a class of problems defined over Boolean functions. Factored zero-$k$-clique is also complete, for a different class of problems. Our hard factored problems are also simple enough that we can reduce them to many other problems, e.g.~to edit distance, $k$-LCS and versions of Max-Flow. We further consider counting variants of the factored problems and give WCtoACFG reductions for them for a natural distribution. Through FGC reductions we then get average-case hardness for well-studied problems like regular expression matching from standard worst-case FGC assumptions. To obtain our WCtoACFG reductions, we formalize the framework of [Boix-Adsera et al. 2019] that was used to give a WCtoACFG reduction for counting $k$-cliques. We define an explicit property of problems such that if a problem has that property one can use the framework on the problem to get a WCtoACFG self reduction. We then use the framework to slightly extend Boix-Adsera et al.'s average-case counting $k$-cliques result to average-case hardness for counting arbitrary subgraph patterns of constant size in $k$-partite graphs...
Object Detection in the Context of Mobile Augmented Reality
Venue: arXiv cs.CV 2020
Year: 2020

In the past few years, numerous Deep Neural Network (DNN) models and frameworks have been developed to tackle the problem of real-time object detection from RGB images. Ordinary object detection approaches process information from the images only, and they are oblivious to the camera pose with regard to the environment and the scale of the environment. On the other hand, mobile Augmented Reality (AR) frameworks can continuously track a camera's pose within the scene and can estimate the correct scale of the environment by using Visual-Inertial Odometry (VIO). In this paper, we propose a novel approach that combines the geometric information from VIO with semantic information from object detectors to improve the performance of object detection on mobile devices. Our approach includes three components: (1) an image orientation correction method, (2) a scale-based filtering approach, and (3) an online semantic map. Each component takes advantage of the different characteristics of the VIO-based AR framework. We implemented the AR-enhanced features using ARCore and the SSD Mobilenet model on Android phones. To validate our approach, we manually labeled objects in image sequences taken from 12 room-scale AR sessions. The results show that our approach can improve on the accuracy of generic object detectors by 12% on our dataset.
Explainability in Deep Reinforcement Learning
Venue: arXiv cs.AI 2020
Year: 2020

A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining models that ingest image source data. However, assessing how XAI techniques can help understand models beyond classification tasks, e.g. for reinforcement learning (RL), has not been extensively studied. We review recent works in the direction to attain Explainable Reinforcement Learning (XRL), a relatively new subfield of Explainable Artificial Intelligence, intended to be used in general public applications, with diverse audiences, requiring ethical, responsible and trustable algorithms. In critical situations where it is essential to justify and explain the agent's behaviour, better explainability and interpretability of RL models could help gain scientific insight on the inner workings of what is still considered a black box. We evaluate mainly studies directly linking explainability to RL, and split these into two categories according to the way the explanations are generated: transparent algorithms and post-hoc explainaility. We also review the most prominent XAI works from the lenses of how they could potentially enlighten the further deployment of the latest advances in RL, in the demanding present and future of everyday problems.
Curriculum Learning for Recurrent Video Object Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Video object segmentation can be understood as a sequence-to-sequence task that can benefit from the curriculum learning strategies for better and faster training of deep neural networks. This work explores different schedule sampling and frame skipping variations to significantly improve the performance of a recurrent architecture. Our results on the car class of the KITTI-MOTS challenge indicate that, surprisingly, an inverse schedule sampling is a better option than a classic forward one. Also, that a progressive skipping of frames during training is beneficial, but only when training with the ground truth masks instead of the predicted ones. Source code and trained models are available at http://imatge-upc.github.io/rvos-mots/.
Description and Technical specification of Cybernetic Transportation Systems: an urban transportation concept
Venue: arXiv cs.RO 2020
Year: 2020

The Cybernetic Transportation Systems (CTS) is an urban mobility concept based on two ideas: the car sharing and the automation of dedicated systems with door-to-door capabilities. In the last decade, many European projects have been developed in this context, where some of the most important are: Cybercars, Cybercars2, CyberMove, CyberC3 and CityMobil. Different companies have developed a first fleet of CTSs in collaboration with research centers around Europe, Asia and America. Considering these previous works, the FP7 project CityMobil2 is on progress since 2012. Its goal is to solve some of the limitations found so far, including the definition of the legal framework for autonomous vehicles on urban environment. This work describes the different improvements, adaptation and instrumentation of the CTS prototypes involved in European cities. Results show tests in our facilities at INRIA-Rocquencourt (France) and the first showcase at León (Spain)
Finding a Shortest Even Hole in Polynomial Time
Venue: arXiv cs.DS 2020
Year: 2020

An even (respectively, odd) hole in a graph is an induced cycle with even (respectively, odd) length that is at least four. Bienstock [DM 1991 and 1992] proved that detecting an even (respectively, odd) hole containing a given vertex is NP-complete. Conforti, Chornuéjols, Kappor, and Vušković [FOCS 1997] gave the first known polynomial-time algorithm to determine whether a graph contains even holes. Chudnovsky, Kawarabayashi, and Seymour [JGT 2005] estimated that Conforti et al.'s algorithm runs in $O(n^{40})$ time on an $n$-vertex graph and reduced the required time to $O(n^{31})$. Subsequently, da~Silva and Vušković~[JCTB 2013], Chang and Lu [JCTB 2017], and Lai, Lu, and Thorup [STOC 2020] improved the time to $O(n^{19})$, $O(n^{11})$, and $O(n^9)$, respectively. The tractability of determining whether a graph contains odd holes has been open for decades until the algorithm of Chudnovsky, Scott, Seymour, and Spirkl [JACM 2020] that runs in $O(n^9)$ time, which Lai et al. also reduced to $O(n^8)$. By extending Chudnovsky et al.'s techniques for detecting odd holes, Chudnovsky, Scott, and Seymour [Combinatorica 2020 to appear] (respectively, [arXiv 2020]) ensured the tractability of finding a long (respectively, shortest) odd hole. They also ensured the NP-hardness of finding a longest odd hole, whose reduction also works for finding a longest even hole. Recently, Cook and Seymour ensured the tractability of finding a long even hole. An intriguing missing piece is the tractability of finding a shortest even hole, left open for at least 15 years by, e.g., Chudnovsky et al. [JGT 2005] and Johnson [TALG 2005]. We resolve this long-standing open problem by giving the first known polynomial-time algorithm, running in $O(n^{31})$ time, for finding a shortest even hole in an $n$-vertex graph that contains even holes.
The Evaluation of Rating Systems in Online Free-for-All Games
Venue: arXiv cs.IR 2020
Year: 2020

Online competitive games have become increasingly popular. To ensure an exciting and competitive environment, these games routinely attempt to match players with similar skill levels. Matching players is often accomplished through a rating system. There has been an increasing amount of research on developing such rating systems. However, less attention has been given to the evaluation metrics of these systems. In this paper, we present an exhaustive analysis of six metrics for evaluating rating systems in online competitive games. We compare traditional metrics such as accuracy. We then introduce other metrics adapted from the field of information retrieval. We evaluate these metrics against several well-known rating systems on a large real-world dataset of over 100,000 free-for-all matches. Our results show stark differences in their utility. Some metrics do not consider deviations between two ranks. Others are inordinately impacted by new players. Many do not capture the importance of distinguishing between errors in higher ranks and lower ranks. Among all metrics studied, we recommend Normalized Discounted Cumulative Gain (NDCG) because not only does it resolve the issues faced by other metrics, but it also offers flexibility to adjust the evaluations based on the goals of the system
Chrome Dino Run using Reinforcement Learning
Venue: arXiv cs.AI 2020
Year: 2020

Reinforcement Learning is one of the most advanced set of algorithms known to mankind which can compete in games and perform at par or even better than humans. In this paper we study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent and finally compare the scores with respect to the episodes and convergence of algorithms with respect to timesteps.
Attributes affecting user decision to adopt a Virtual Private Network (VPN) app
Venue: arXiv cs.CR 2020
Year: 2020

A Virtual Private Network (VPN) helps to mitigate security and privacy risks of data transmitting on unsecured network such as public Wi-Fi. However, despite awareness of public Wi-Fi risks becoming increasingly common, the use of VPN when using public Wi-Fi is low. To increase adoption, understanding factors driving user decision to adopt a VPN app is an important first step. This study is the first to achieve this objective using discrete choice experiments (DCEs) to elicit individual preferences of specific attributes of a VPN app. The experiments were run in the United Kingdom (UK) and Japan (JP). We first interviewed participants (15 UK, 17 JP) to identify common attributes of a VPN app which they considered important. The results were used to design and run a DCE in each country. Participants (149 UK, 94 JP) were shown a series of two hypothetical VPN apps, varying in features, and were asked to choose one which they preferred. Customer review rating, followed by price of a VPN app, significantly affected the decision to choose which VPN app to download and install. A change from a rating of 3 to 4-5 stars increased the probability of choosing an app by 33% in the UK and 14% in Japan. Unsurprisingly, price was a deterrent. Recommendations by friends, source of product reviews, and the presence of in-app ads also played a role but to a lesser extent. To actually use a VPN app, participants considered Internet speed, connection stability, battery level on mobile devices, and the presence of in-app ads as key drivers. Participants in the UK and in Japan prioritized these attributes differently, suggesting possible influences from cultural differences.
TextDecepter: Hard Label Black Box Attack on Text Classifiers
Venue: arXiv cs.CL 2020
Year: 2020

Machine learning has been proven to be susceptible to carefully crafted samples, known as adversarial examples. The generation of these adversarial examples helps to make the models more robust and gives us an insight into the underlying decision-making of these models. Over the years, researchers have successfully attacked image classifiers in both, white and black-box settings. However, these methods are not directly applicable to texts as text data is discrete. In recent years, research on crafting adversarial examples against textual applications has been on the rise. In this paper, we present a novel approach for hard-label black-box attacks against Natural Language Processing (NLP) classifiers, where no model information is disclosed, and an attacker can only query the model to get a final decision of the classifier, without confidence scores of the classes involved. Such an attack scenario applies to real-world black-box models being used for security-sensitive applications such as sentiment analysis and toxic content detection.
Discouraging Pool Block Withholding Attacks in Bitcoins
Venue: arXiv cs.GT 2020
Year: 2020

The arisen of Bitcoin has led to much enthusiasm for blockchain research and block mining, and the extensive existence of mining pools helps its participants (i.e., miners) gain reward more frequently. Recently, the mining pools are proved to be vulnerable for several possible attacks, and pool block withholding attack is one of them: one strategic pool manager sends some of her miners to other pools and these miners pretend to work on the puzzles but actually do nothing. And these miners still get reward since the pool manager can not recognize these malicious miners. In this work, we revisit the game-theoretic model for pool block withholding attacks and propose a revised approach to reallocate the reward to the miners. Fortunately, in the new model, the pool managers have strong incentive to not launch such attacks. We show that for any number of mining pools, no-pool-attacks is always a Nash equilibrium. Moreover, with only two minority mining pools participating, no-pool-attacks is actually the unique Nash equilibrium.
Dependability Evaluation of Middleware Technology for Large-scale Distributed Caching
Venue: arXiv cs.SE 2020
Year: 2020

Distributed caching systems (e.g., Memcached) are widely used by service providers to satisfy accesses by millions of concurrent clients. Given their large-scale, modern distributed systems rely on a middleware layer to manage caching nodes, to make applications easier to develop, and to apply load balancing and replication strategies. In this work, we performed a dependability evaluation of three popular middleware platforms, namely Twemproxy by Twitter, Mcrouter by Facebook, and Dynomite by Netflix, to assess availability and performance under faults, including failures of Memcached nodes and congestion due to unbalanced workloads and network link bandwidth bottlenecks. We point out the different availability and performance trade-offs achieved by the three platforms, and scenarios in which few faulty components cause cascading failures of the whole distributed system.
Improving Services Offered by Internet Providers by Analyzing Online Reviews using Text Analytics
Venue: arXiv cs.CY 2020
Year: 2020

With the proliferation of digital infrastructure, there is a plethora of demand for internet services, which makes the wireless communications industry highly competitive. Thus internet service providers (ISPs) must ensure that their efforts are targeted towards attracting and retaining customers to ensure continued growth. As Web 2.0 has gained traction and more tools have become available, customers in recent times are equipped to make well-informed decisions, specifically due to the colossal information available in online reviews. ISPs can use this information to better understand the views of the customers about their products and services. The goal of this paper is to identify the current strengths, weaknesses, opportunities, and threats (SWOT) of each ISP by exploring consumer reviews using text analytics. The proposed approach consists of four different stages: bigram and trigram analyses, topic identification, SWOT analysis and Root Cause Analysis (RCA). For each ISP, we first categorize online reviews into positive and negative based on customer ratings and then leverage text analytic tools to determine the most frequently used and co-occurring words in each categorization of reviews. Subsequently, looking at the positive and negative topics in each ISP, we conduct the SWOT analysis as well as the RCA to help companies identify the internal and external factors impacting customer satisfaction. We use a case study to illustrate the proposed approach. The proposed managerial insights that are derived from the results can act as a decision support tool for ISPs to offer better products and services for their customers.
OpenFraming: We brought the ML; you bring the data. Interact with your data and discover its frames
Venue: arXiv cs.CL 2020
Year: 2020

When journalists cover a news story, they can cover the story from multiple angles or perspectives. A news article written about COVID-19 for example, might focus on personal preventative actions such as mask-wearing, while another might focus on COVID-19's impact on the economy. These perspectives are called "frames," which when used may influence public perception and opinion of the issue. We introduce a Web-based system for analyzing and classifying frames in text documents. Our goal is to make effective tools for automatic frame discovery and labeling based on topic modeling and deep learning widely accessible to researchers from a diverse array of disciplines. To this end, we provide both state-of-the-art pre-trained frame classification models on various issues as well as a user-friendly pipeline for training novel classification models on user-provided corpora. Researchers can submit their documents and obtain frames of the documents. The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents. The code making up our system is also open-sourced and well-documented, making the system transparent and expandable. The system is available on-line at http://www.openframing.org and via our GitHub page https://github.com/davidatbu/openFraming .
A Self-supervised GAN for Unsupervised Few-shot Object Recognition
Venue: arXiv cs.CV 2020
Year: 2020

This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled, and test images are divided into queries and a few labeled support images per object class of interest. The training and test images do not share object classes. We extend the vanilla GAN with two loss functions, both aimed at self-supervised learning. The first is a reconstruction loss that enforces the discriminator to reconstruct the probabilistically sampled latent code which has been used for generating the "fake" image. The second is a triplet loss that enforces the discriminator to output image encodings that are closer for more similar images. Evaluation, comparisons, and detailed ablation studies are done in the context of few-shot classification. Our approach significantly outperforms the state of the art on the Mini-Imagenet and Tiered-Imagenet datasets.